The empirical approach to cognition
has traditionally been dominated by
classical probability theory
However, these approaches has been
unable to explain some cognitive processes
For example, the skill of remembering
consecutive digits cannot be quantified
by classical statistical tools
such as Shannon entropy
because they cannot recognize
the algorithmic nature of a sequence
that the mind can easily recognize
and recall
What was needed was a much more
powerful, theoretical framework,
and the introduction of new tools
based upon the theory of algorithmic probability
That is, the ultimate theory
of induction and inference,
formally connecting complexity and probability
by way of universal computation.
For example, if a sequence of events
can be encoded by a short algorithm,
then a mind can understand and compress
that sequence even if it appears random
to traditional statistical tools.
This means, that statistical tools,
such as Shannon entropy applied to
inputs, outputs and internal representations
are not suitable to describe or explain
sophisticated mental processes.
However, the theory of algorithmic probability
applied to human and animal cognition
provides the theoretical means
to study these sophisticated processes
and even explains what was
traditionally seen as positive
or negative biases in terms of
choices that a mind takes.
For example, humans will tend to
find patterns in random data
that the traditional view will mostly
account for in the form of biased decisions
based on past experiences rather than
as a consequence of algorithmic process
of the mind trying to find sense and logic
even when there is none.
We have developed these tools
based on the principle that
a mind is better explained not by
statistical processes, biased by past experiences,
but as an algorithmic probability estimator
that has evolved to learn and exploit
the algorithmic nature of the world.
Paradoxically, this means the mind
may actually behave less often
as an irrational being,
biased by personal experiences,
and more like a general sophisticated,
predictable algorithm.
Indeed, early cognition research
found analogies with computation.
But soon found such analogy
overly simplistic.
Today, however, the bridge
between the mind and computers
we have made is more subtle,
and more sophisticated.
The bridge consists a model of the brain
that conceives the mind
as an idealized universal algorithm,
capable of generating models.
Our conceptual framework
unveils evidence supporting
these mechanisms of cognition.
We have shown that
landmark experiments on animals
can be confirmed by
simply quantifying the complexity
of behaviour time series from
different studies.
For example, ants maximize communication
efficiency as a function of sequence complexity.
Fruit flies do not behave in the simplistic
ways that were previous thought.
Rodents harness the power of randomness
when outsmarted by artificial intelligence.
And, humans are most creative
at generating randomness when
they reach 25.
These kinds of discoveries were,
however, impossible to make before
with traditional tools and classical measures,
and they foreshadow algorithmic cognition
as an exciting new field of research to tackle
some of the tough pressing issues,
related to mental disorders
and neurodegenerative diseases.